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New Relic Updates SRE Agent

New Relic announced a series of innovations that operationalize AI across the enterprise, empowering teams to focus on business growth instead of firefighting. 

Bolstered by cutting-edge deterministic analytical tools and a suite of new AIOps capabilities, New Relic’s SRE Agent provides next-generation issue triage, change management, incident lifecycle management, intelligent root cause analysis and other features to help engineers cut through data noise and boost operational stability.

“AI is pushing software development beyond human scale, creating a surge of system changes and telemetry volume that IT teams can no longer manage on their own,” said New Relic Chief Product Officer Brian Emerson. “Observability must evolve from simply surfacing data to analyzing it and helping humans take action with less toil. With the new SRE Agent that draws on our powerful AI-strengthened observability capabilities, we’re providing engineers with agentic teammates grounded in live data to resolve incidents faster and with fewer mistakes. The enterprises that win in this era will be those that use AI to cut through noise and optimize business uptime.”

The New Relic SRE Agent helps customers shift operations from reactive to proactive by deploying “always on” AI teammates that diagnose incidents and recommend next steps oftentimes before an engineer acknowledges a page. The agent acts as a specialized worker that performs deep, full-stack diagnostics, combining the flexibility and reasoning capabilities of generative models with “ground truth” brought by a suite of finely-honed deterministic features, such as causal graphs, incident data, performance antipattern knowledge and customer-developed workflows.

The SRE Agent acts as an intelligent context engine for the incident lifecycle. Through Slack and Zoom integrations, responders can query New Relic directly from triage rooms while the SRE Agent captures human context to power automated fact finding, root cause analysis, impact assessments, and reporting. Users gain a unified view of the evolving timeline of events that led up to and following an incident. As a result, they can measure user impact in real-time, identify duplicate incidents, and generate and refine comprehensive post-incident reports.  

The New Relic SRE Agent draws on new Intelligent Observability Platform capabilities including:

  • Intelligent RCA (iRCA): iRCA cuts through the noise by automatically searching the entity's topology graph, scoring the graph using probabilistic causal models, and applying a path-based ranking algorithm to narrow down the problem space in seconds, not hours. By leveraging iRCA, the SRE Agent performs its most time consuming task —separating noise from signal—in high-confidence, deterministic methodologies.
  • Workflow Automation: Intelligent automation that enables teams to automate complex or repetitive operational tasks by creating workflows—structured, multi-step processes that can include conditional logic, human approvals, and integrations with external tools, without writing additional code. DevOps and SREs can improve efficiency by automating everything from notification routing and post-deployment health checks to complex processes such as EC2 instance resizing or Lambda function rollbacks. The SRE Agent will be able to invoke workflows but also be invoked as part of a workflow, which adds an almost endless potential for customization and utility to the mix.

Additional AIOps capabilities now available include:

  • Performance Risks Inbox: Goes beyond reactive application performance monitoring (APM) to show why an incident or outage is about to happen so action can quickly be taken. Proactively detects and groups critical coding anti-patterns, including slow SQL queries, N+1 queries, excessive database queries, and the like which threaten application stability and business continuity.
  • Smart Alerts: An automated alerting engine that uses AI-strengthened anomaly detection and dynamic baselines to reduce alert noise and improve signal quality across complex environments. By delivering more reliable, behavior-aware alerts, it helps teams respond faster and with greater confidence. Use of the capability also lays the groundwork for businesses to maximize agentic AI, ensuring alerts are automated for better deployment of digital workforces.

These innovations are now available in preview to customers as part of the New Relic Intelligent Observability Platform. Workflow Automation is now generally available. 

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

New Relic Updates SRE Agent

New Relic announced a series of innovations that operationalize AI across the enterprise, empowering teams to focus on business growth instead of firefighting. 

Bolstered by cutting-edge deterministic analytical tools and a suite of new AIOps capabilities, New Relic’s SRE Agent provides next-generation issue triage, change management, incident lifecycle management, intelligent root cause analysis and other features to help engineers cut through data noise and boost operational stability.

“AI is pushing software development beyond human scale, creating a surge of system changes and telemetry volume that IT teams can no longer manage on their own,” said New Relic Chief Product Officer Brian Emerson. “Observability must evolve from simply surfacing data to analyzing it and helping humans take action with less toil. With the new SRE Agent that draws on our powerful AI-strengthened observability capabilities, we’re providing engineers with agentic teammates grounded in live data to resolve incidents faster and with fewer mistakes. The enterprises that win in this era will be those that use AI to cut through noise and optimize business uptime.”

The New Relic SRE Agent helps customers shift operations from reactive to proactive by deploying “always on” AI teammates that diagnose incidents and recommend next steps oftentimes before an engineer acknowledges a page. The agent acts as a specialized worker that performs deep, full-stack diagnostics, combining the flexibility and reasoning capabilities of generative models with “ground truth” brought by a suite of finely-honed deterministic features, such as causal graphs, incident data, performance antipattern knowledge and customer-developed workflows.

The SRE Agent acts as an intelligent context engine for the incident lifecycle. Through Slack and Zoom integrations, responders can query New Relic directly from triage rooms while the SRE Agent captures human context to power automated fact finding, root cause analysis, impact assessments, and reporting. Users gain a unified view of the evolving timeline of events that led up to and following an incident. As a result, they can measure user impact in real-time, identify duplicate incidents, and generate and refine comprehensive post-incident reports.  

The New Relic SRE Agent draws on new Intelligent Observability Platform capabilities including:

  • Intelligent RCA (iRCA): iRCA cuts through the noise by automatically searching the entity's topology graph, scoring the graph using probabilistic causal models, and applying a path-based ranking algorithm to narrow down the problem space in seconds, not hours. By leveraging iRCA, the SRE Agent performs its most time consuming task —separating noise from signal—in high-confidence, deterministic methodologies.
  • Workflow Automation: Intelligent automation that enables teams to automate complex or repetitive operational tasks by creating workflows—structured, multi-step processes that can include conditional logic, human approvals, and integrations with external tools, without writing additional code. DevOps and SREs can improve efficiency by automating everything from notification routing and post-deployment health checks to complex processes such as EC2 instance resizing or Lambda function rollbacks. The SRE Agent will be able to invoke workflows but also be invoked as part of a workflow, which adds an almost endless potential for customization and utility to the mix.

Additional AIOps capabilities now available include:

  • Performance Risks Inbox: Goes beyond reactive application performance monitoring (APM) to show why an incident or outage is about to happen so action can quickly be taken. Proactively detects and groups critical coding anti-patterns, including slow SQL queries, N+1 queries, excessive database queries, and the like which threaten application stability and business continuity.
  • Smart Alerts: An automated alerting engine that uses AI-strengthened anomaly detection and dynamic baselines to reduce alert noise and improve signal quality across complex environments. By delivering more reliable, behavior-aware alerts, it helps teams respond faster and with greater confidence. Use of the capability also lays the groundwork for businesses to maximize agentic AI, ensuring alerts are automated for better deployment of digital workforces.

These innovations are now available in preview to customers as part of the New Relic Intelligent Observability Platform. Workflow Automation is now generally available. 

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...